
# Created by: Sergiu Iatco / 2021.10.01
# https://github.com/itsergiu/Predict-S-P-500-correction-with-Shiller-PE-Ratio
# How to predict S&P 500 correction

# Nadeem Walayat - The Market Oracle
# http://www.marketoracle.co.uk/Article69423.html

# Vitaliy Katsenelson 
# https://contrarianedge.com/sideways-market/

# https://www.marketwatch.com/story/market-analysts-cant-agree-on-where-stocks-are-going-next-so-double-check-the-data-before-you-buy-or-sell-11632447577

# Purpose: to build a machine learning model to predict S&P 500 correction within next 6 months


# !pip install pandas
# !pip install dateparser
# !pip install xgboost
# !pip install sklearn
# !pip install numpy
# !pip install plotly_express


import pandas as pd
# import dateparser
import xgboost as xgb
from sklearn import model_selection
from sklearn.metrics import r2_score
import numpy as np
from fredapi import Fred
import plotly.express as px
from IPython.display import display


url_per='https://www.multpl.com/shiller-pe/table/by-month'


ls_tables = pd.read_html(url_per)


type(ls_tables)

list


len(ls_tables)

1


ls_tables[0]


df_per=ls_tables[0]


df_per.dtypes

Date            object
Value Value    float64
dtype: object


cols = df_per.columns.tolist()
cols

['Date', 'Value Value']


df_per.rename(
    columns=({ cols[1]: 'PER'}), 
    inplace=True,)


df_per.head()


url_sp='https://www.multpl.com/s-p-500-historical-prices/table/by-month'


ls_tables = pd.read_html(url_sp)


len(ls_tables)

1


df_sp=ls_tables[0]
df_sp.head()


cols = df_sp.columns.tolist()
cols

['Date', 'Price Value']


df_sp.rename(
    columns=({ cols[1]: 'Price'}), 
    inplace=True,)


df_sp.head()


df_per.index, df_sp.index

(RangeIndex(start=0, stop=1825, step=1),
 RangeIndex(start=0, stop=1826, step=1))


df = df_per.merge(df_sp, left_on='Date', right_on='Date')
df


df.dtypes

Date      object
PER      float64
Price    float64
dtype: object


df['Date']=pd.to_datetime(df['Date'])
df


df.head()


df.set_index(['Date'], inplace=True)
df['Date']=df.index
df


# https://pandas.pydata.org/docs/reference/api/pandas.melt.html
df_per = pd.melt(df, id_vars=['Date'], value_vars=['PER'])
px.line(df_per, x='Date', y='value', color='variable')


df.describe()


df.dtypes

PER             float64
Price           float64
Date     datetime64[ns]
dtype: object


ls_price_p = ['Price'+str(i)+'F' for i in range(7)]
ls_price_p

['Price0F', 'Price1F', 'Price2F', 'Price3F', 'Price4F', 'Price5F', 'Price6F']


ls_price_p[1:]

['Price1F', 'Price2F', 'Price3F', 'Price4F', 'Price5F', 'Price6F']


ls_price_p[0]

'Price0F'


df.columns.tolist()

['PER', 'Price', 'Date']


df.head(10)


df.tail(10)


for i,e in enumerate(ls_price_p):
    df[e]=df.Price.shift(i)


df['Price1P'] = df['Price'].shift(-1)
df['Price_Var']=df['Price']/df['Price1P']-1


df.head(10)


df['PriceFmin']=df[ls_price_p].min(axis=1)


df.head(10)


col_y = 'Price_Corr_6M'
df[col_y]=1-df['PriceFmin']/df['Price0F']


df.head(10)


df[df.isna().any(axis=1)]


df.loc[df.isna().any(axis=1),col_y]=np.nan # set to zero corrections with Nan rows
df[df.isna().any(axis=1)]


df_initial=df.copy()


df_initial.head()


df.dropna(axis=0, how='any', inplace=True) # remove NaN rowss


df[df[col_y]>0].describe()


last_months=240
df_per = pd.melt(df.head(last_months), id_vars='Date', value_vars=['Price'])
px.line(df_per, x='Date', y='value', color='variable')


# min_correction=0.15
min_correction=0.15 # take a look at big corrections
df[df[col_y]>min_correction].head(10)


df[df[col_y]>min_correction][col_y].describe()

count    149.000000
mean       0.228507
std        0.072260
min        0.150127
25%        0.175627
50%        0.205418
75%        0.254902
max        0.469682
Name: Price_Corr_6M, dtype: float64


# plot PER & Price_correction


min_correction=0.05 # force learning above ?
# min_correction=0
if min_correction!=0:
    df[col_y] = df[col_y].apply(lambda x: 0 if x<min_correction else x)

# df[col_y] = -df[col_y] # reverse sign

df_per_pc=df[['Date','PER',col_y]].copy()
df_per_pc[[col_y]]=100*df_per_pc[[col_y]]
df_per_pc[col_y]=-df_per_pc[col_y] # reverse sign for plotting


last_months=2400
df_plot = pd.melt(df_per_pc, id_vars='Date', value_vars=['PER',col_y])
px.line(df_plot, x='Date', y='value', color='variable')


pd.set_option('display.max_columns', 500)
df.head(10)


df.head(36)


def gen_features(df,cols, ref_period, band):
    features = []
    
    features.append(df['Date'].iloc[ref_period])
    for col in cols:
        segment = df[col].iloc[ref_period:ref_period+band]
        features.append(segment.min()) 
        features.append(segment.max())

        for i in range(2,99,10):
            features.append(np.quantile(segment, i/100))

        features.append(segment.mean())
        features.append(segment.std())
        features.append(segment.mad())
        features.append(segment.skew())
        features.append(segment.kurtosis())

        features.append(np.sqrt(np.mean(segment**2)))

        features.append(np.abs(segment).mean())
        features.append(np.abs(segment).std())

        features.append(np.abs(np.diff(segment)).mean())
        features.append(np.abs(np.diff(segment)).std())
        
    return pd.Series(features)


def generate_dataset(df_f, cols, band=60, start_date='2000-01-01', periods = 0):
#     global cols
    df = df_f.copy()
    X_train = pd.DataFrame()

    y_train = pd.Series(dtype='float64', name=col_y)
#     cols =['PER', 'Price', 'Price_Var']

    if periods>0:
        ref_periods = periods
    else:
        ref_periods = df[df.index>=start_date].shape[0]
        
    for ref_period in range(ref_periods):
        X = gen_features(df,cols, ref_period, band)
        X_train = X_train.append(X, ignore_index=True)
        y_train = y_train.append(pd.Series(df[col_y][ref_period], index=[df.index[ref_period]], name=col_y))

    X_train = X_train.rename({0:'Date'}, axis='columns')
    X_train = X_train.set_index('Date')
    return X_train, y_train


start_date = '1950-01-01' # set your own date
band=60 # how many months from past to use to generate statistics


cols_features = ['PER', 'Price', 'Price_Var']
X_train, y_train = generate_dataset(df, cols_features, band, start_date)


y_train.shape, X_train.shape

((872,), (872, 66))


y_train.index

DatetimeIndex(['2022-08-01', '2022-07-01', '2022-06-01', '2022-05-01',
               '2022-04-01', '2022-03-01', '2022-02-01', '2022-01-01',
               '2021-12-01', '2021-11-01',
               ...
               '1950-10-01', '1950-09-01', '1950-08-01', '1950-07-01',
               '1950-06-01', '1950-05-01', '1950-04-01', '1950-03-01',
               '1950-02-01', '1950-01-01'],
              dtype='datetime64[ns]', length=872, freq=None)


X_train.index

DatetimeIndex(['2022-08-01', '2022-07-01', '2022-06-01', '2022-05-01',
               '2022-04-01', '2022-03-01', '2022-02-01', '2022-01-01',
               '2021-12-01', '2021-11-01',
               ...
               '1950-10-01', '1950-09-01', '1950-08-01', '1950-07-01',
               '1950-06-01', '1950-05-01', '1950-04-01', '1950-03-01',
               '1950-02-01', '1950-01-01'],
              dtype='datetime64[ns]', name='Date', length=872, freq=None)


dataset = pd.concat([y_train, X_train], axis=1, join="inner") # align to avoid random shifting
dataset.describe()


Y=dataset.pop(col_y) # target to predict


Y.describe()

count    872.000000
mean       0.030044
std        0.059539
min        0.000000
25%        0.000000
50%        0.000000
75%        0.053825
max        0.377846
Name: Price_Corr_6M, dtype: float64


X=dataset # features for prediction
X.describe()


# PREDICTION


def generate_model(df, cols, start_date, verbosity = False):
    best_score = 0
    best_model = None
    best_band = 0
    test_size = 0.3
    bands = 60
    
    for band in range(12,bands+1,12): # [12,24,36,48,60] months
        X_train, y_train = generate_dataset(df, cols, band, start_date)
        dataset = pd.concat([y_train, X_train], axis=1, join="inner") # align to avoid random shifting
        Y=dataset.pop(col_y)
        X=dataset
        X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, shuffle = True)
        model = xgb.XGBRegressor()
        model.fit(X_train,Y_train.values)
        score = model.score(X_test, Y_test)
        if best_score < score:
            best_score = score
            best_model = model
            best_band = band
            X_t = X_test
            Y_t = Y_test
            
#         if verbosity: # verbosit for each band
#             print(score, band)
    if verbosity: # verbosity for best score
        print('retest score:', best_model.score(X_t, Y_t).round(3), best_score.round(3), 'features:', cols, 'X_train.shape:', X_train.shape)# retest best model score
    
    return best_model, best_band, best_score


start_date = '1970-01-01'
cols_features =['PER', 'Price', 'Price_Var']
model, band, score = generate_model(df, cols_features, start_date, True)

retest score: 0.799 0.799 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)


# model.score(X_t, Y_t), best_score # retest best model score


df_initial['horizon'] = 'past'
df_initial.loc[np.isnan(df_initial['Price6F']),['horizon']]='future'
df_initial.head(10)


df_initial['Date']=df_initial.index # add required column
df_initial.head(24)


df.head()


periods=120 # predicted periods
df_pred = df_initial.copy()
cols_features =['PER', 'Price', 'Price_Var']
X_pred, y_dummy = generate_dataset(df_pred, cols_features, band, periods=periods)
X_pred.head() # generated features


X_pred.shape

(120, 66)


# df_pred = df_pred.iloc[:periods]
# df_pred


y_pred = model.predict(X_pred).round(2) # readable
# y_pred = model.predict(X_pred)
# y_pred


df_pred = df_pred.iloc[:periods].copy()
df_pred[col_y+'Pred'] = np.clip(y_pred,0,0.5) # replace outliers


cols_pred = ['PER', 'Price', 'Price_Corr_6M', 'horizon', 'Price_Corr_6MPred']
df_pred[cols_pred].head(36) # one shoot prediction


ind_y = df_pred['horizon']=='past'
# ind_y


model.score(X_pred.loc[ind_y], df_pred.loc[ind_y,col_y]) # score for past - could contain seen data (X_train)

0.7946193676991046


# ind_y[ind_y==True] # past months for score


# RANDOM CROSS FOLD
n_times = 10 # build model n times and predict n times
periods = 6 # predicted last months
df_pred = df_initial.copy()
df_pred_iter = df_pred.iloc[:periods].copy()
ls_pc6m_cols=[]
ls_score = []
cols_features =['PER', 'Price', 'Price_Var']
for i in range(n_times):
    start_date = '1970-01-01'
    model, band, score = generate_model(df, cols_features, start_date, True)
    ls_score.append(score.round(3))
    print('score:',score.round(3), 'band:', band)
    
    X_pred, y_dummy = generate_dataset(df_pred, cols_features, band, periods=periods)
    y_pred = model.predict(X_pred).round(2) # readable
    pc6m_col = 'PC6M'+str(1+i)
    ls_pc6m_cols.append(pc6m_col)
    df_pred_iter[pc6m_col] = y_pred

retest score: 0.759 0.759 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.759 band: 60
retest score: 0.799 0.799 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.799 band: 60
retest score: 0.787 0.787 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.787 band: 60
retest score: 0.729 0.729 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.729 band: 48
retest score: 0.706 0.706 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.706 band: 24
retest score: 0.815 0.815 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.815 band: 48
retest score: 0.774 0.774 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.774 band: 60
retest score: 0.758 0.758 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.758 band: 36
retest score: 0.712 0.712 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.712 band: 24
retest score: 0.834 0.834 features: ['PER', 'Price', 'Price_Var'] X_train.shape: (442, 66)
score: 0.834 band: 60


cols_iter = ['PER', 'Price', 'Price_Var', 'horizon']
df_pred_iter=df_pred_iter[cols_iter+ls_pc6m_cols].copy()


df_pred_iter['PC6M_AVG']=df_pred_iter[ls_pc6m_cols].mean(axis=1) # average of all predictions


# RANDOM CROSS FOLD PREDICTION with ['PER', 'Price', 'Price_Var']
df_pred_iter


np.mean(ls_score), ls_score # model score mean and model score list

(0.7672999999999999,
 [0.759, 0.799, 0.787, 0.729, 0.706, 0.815, 0.774, 0.758, 0.712, 0.834])


# Buffet Indicator


# Get your api_key from FRED
# https://fred.stlouisfed.org/docs/api/api_key.html

fred = Fred(api_key='abcdefghijklmnopqrstuvwxyz123456') # example # get your api_key from FRED


# wilshire = fred.get_series('WILL5000PRFC')


gdp_data = fred.get_series_latest_release('GDP')
gdp_data

1946-01-01          NaN
1946-04-01          NaN
1946-07-01          NaN
1946-10-01          NaN
1947-01-01      243.164
                ...    
2021-07-01    23550.420
2021-10-01    24349.121
2022-01-01    24740.480
2022-04-01    25248.476
2022-07-01    25723.941
Length: 307, dtype: float64


# Convert from quarter to day with interpolate
idx = pd.date_range(gdp_data.index.min(), gdp_data.index.max()) 
gdp_data = gdp_data.reindex(idx, fill_value=np.nan).copy()
gdp_data

1946-01-01          NaN
1946-01-02          NaN
1946-01-03          NaN
1946-01-04          NaN
1946-01-05          NaN
                ...    
2022-06-27          NaN
2022-06-28          NaN
2022-06-29          NaN
2022-06-30          NaN
2022-07-01    25723.941
Freq: D, Length: 27941, dtype: float64


gdp_data = pd.to_numeric(gdp_data, errors='coerce').astype('float64').copy()
gdp_data.interpolate(inplace=True)
gdp_data

1946-01-01            NaN
1946-01-02            NaN
1946-01-03            NaN
1946-01-04            NaN
1946-01-05            NaN
                 ...     
2022-06-27    25703.04144
2022-06-28    25708.26633
2022-06-29    25713.49122
2022-06-30    25718.71611
2022-07-01    25723.94100
Freq: D, Length: 27941, dtype: float64


# %matplotlib inline
gdp_data.plot.line()

<AxesSubplot:>


wilshire = fred.get_series('WILL5000PRFC')


idx = pd.date_range(wilshire.index.min(), wilshire.index.max())
wilshire = wilshire.reindex(idx, fill_value=np.nan).copy()
wilshire

1970-12-31      830.27
1971-01-01         NaN
1971-01-02         NaN
1971-01-03         NaN
1971-01-04         NaN
                ...   
2023-01-08         NaN
2023-01-09    38648.30
2023-01-10    38950.46
2023-01-11    39470.43
2023-01-12    39663.09
Freq: D, Length: 19006, dtype: float64


wilshire.interpolate(inplace=True)
wilshire

1970-12-31      830.270000
1971-01-01      831.754138
1971-01-02      833.238276
1971-01-03      834.722414
1971-01-04      836.206552
                  ...     
2023-01-08    38642.433333
2023-01-09    38648.300000
2023-01-10    38950.460000
2023-01-11    39470.430000
2023-01-12    39663.090000
Freq: D, Length: 19006, dtype: float64


wilshire.plot.line()

<AxesSubplot:>


gdp_data.name='GDP'
wilshire.name='Wilshire 5000'
bf_ind = pd.concat([gdp_data, wilshire], axis=1) # Buffet Indicator
bf_ind


bf_ind.dropna(subset=['Wilshire 5000'], inplace=True) # drop missing GDP
bf_ind


bf_ind.interpolate(method ='spline',order=2, inplace=True)


col_bf_ind='Buffet Indicator'
bf_ind[col_bf_ind] =  bf_ind['Wilshire 5000'] / bf_ind['GDP'] 
bf_ind


# https://www.longtermtrends.net/market-cap-to-gdp-the-buffett-indicator/
bf_ind[col_bf_ind].plot.line() # compare with chart from above link

<AxesSubplot:>


bf_ind.index.min(), bf_ind.index.max()

(Timestamp('1970-12-31 00:00:00', freq='D'),
 Timestamp('2023-01-12 00:00:00', freq='D'))


df_initial


df_pred = df_initial.copy()
df_pred = pd.concat([df_pred, bf_ind], axis=1)
df_pred.sort_index(ascending=False, inplace=True)


cols_iter = ['Date','PER', 'Price', 'Price_Var', 'horizon', 'Buffet Indicator', 'Price_Corr_6M']
df_pred[col_bf_ind] = df_pred[col_bf_ind].interpolate(method='linear', limit_direction='backward')
df_pred = df_pred[cols_iter].copy()
df_pred.dropna(subset=['horizon', 'Buffet Indicator'], inplace=True)
df_pred


cols_train=['Date','PER', 'Price', 'Price_Var', 'Buffet Indicator', 'Price_Corr_6M']
df= df_pred[cols_train].copy()
df.dropna(inplace=True)
df


# RANDOM CROSS FOLD for Bootstrapping
ls_cols_features=['PER', 'Price', 'Price_Var','Buffet Indicator'] # test with one by one feature
ls_cols_features.append(['PER','Buffet Indicator'])  # test with set of features
ls_cols_features.append(['PER', 'Price','Buffet Indicator'])  # test with set of features
start_date = df.index[-periods]

for e in ls_cols_features:
    n_times = 10 # build model n times and predict n times
    periods = 6 # predicted last months
    # df_pred = df_initial.copy()
    df_pred_iter = df_pred.iloc[:periods].copy()
    ls_pc6m_cols=[]
    ls_score = []
    cols_features = [e]
    if isinstance(e, list):
        cols_features = e # set of features
    else:
        cols_features = [e] # one feature
    
    for i in range(n_times):
    #     start_date = '1974-01-01'
        model, band, score = generate_model(df, cols_features, start_date, True)
        score = score.round(3)
        ls_score.append(score)
        print('score:',score, 'band:', band)

        X_pred, y_dummy = generate_dataset(df_pred, cols_features, band, periods=periods)
        y_pred = model.predict(X_pred).round(2) # recols_features, adable
        y_pred = np.clip(y_pred,0,0.5) # replace outliers
        pc6m_col = 'PC6M'+str(1+i)
        ls_pc6m_cols.append(pc6m_col)
        df_pred_iter[pc6m_col] = y_pred  

    # RANDOM CROSS FOLD PREDICTION on PER and BUFFET INDICATOR
    df_pred_iter['PC6M_AVG']=df_pred_iter[ls_pc6m_cols].mean(axis=1) # average of all predictions
    display(df_pred_iter.loc[:,df_pred_iter.columns!=col_y])
    print('scores:', np.mean(ls_score).round(3), ls_score) # model score mean and model score list)

retest score: 0.754 0.754 features: ['PER'] X_train.shape: (430, 22)
score: 0.754 band: 36
retest score: 0.729 0.729 features: ['PER'] X_train.shape: (430, 22)
score: 0.729 band: 48
retest score: 0.748 0.748 features: ['PER'] X_train.shape: (430, 22)
score: 0.748 band: 60
retest score: 0.746 0.746 features: ['PER'] X_train.shape: (430, 22)
score: 0.746 band: 36
retest score: 0.778 0.778 features: ['PER'] X_train.shape: (430, 22)
score: 0.778 band: 24
retest score: 0.763 0.763 features: ['PER'] X_train.shape: (430, 22)
score: 0.763 band: 36
retest score: 0.788 0.788 features: ['PER'] X_train.shape: (430, 22)
score: 0.788 band: 60
retest score: 0.641 0.641 features: ['PER'] X_train.shape: (430, 22)
score: 0.641 band: 24
retest score: 0.72 0.72 features: ['PER'] X_train.shape: (430, 22)
score: 0.72 band: 60
retest score: 0.64 0.64 features: ['PER'] X_train.shape: (430, 22)
score: 0.64 band: 12

scores: 0.731 [0.754, 0.729, 0.748, 0.746, 0.778, 0.763, 0.788, 0.641, 0.72, 0.64]
retest score: 0.799 0.799 features: ['Price'] X_train.shape: (430, 22)
score: 0.799 band: 24
retest score: 0.781 0.781 features: ['Price'] X_train.shape: (430, 22)
score: 0.781 band: 60
retest score: 0.76 0.76 features: ['Price'] X_train.shape: (430, 22)
score: 0.76 band: 60
retest score: 0.702 0.702 features: ['Price'] X_train.shape: (430, 22)
score: 0.702 band: 60
retest score: 0.788 0.788 features: ['Price'] X_train.shape: (430, 22)
score: 0.788 band: 24
retest score: 0.768 0.768 features: ['Price'] X_train.shape: (430, 22)
score: 0.768 band: 48
retest score: 0.813 0.813 features: ['Price'] X_train.shape: (430, 22)
score: 0.813 band: 60
retest score: 0.746 0.746 features: ['Price'] X_train.shape: (430, 22)
score: 0.746 band: 60
retest score: 0.759 0.759 features: ['Price'] X_train.shape: (430, 22)
score: 0.759 band: 48
retest score: 0.837 0.837 features: ['Price'] X_train.shape: (430, 22)
score: 0.837 band: 48

scores: 0.775 [0.799, 0.781, 0.76, 0.702, 0.788, 0.768, 0.813, 0.746, 0.759, 0.837]
retest score: 0.626 0.626 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.626 band: 36
retest score: 0.732 0.732 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.732 band: 36
retest score: 0.712 0.712 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.712 band: 36
retest score: 0.797 0.797 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.797 band: 36
retest score: 0.712 0.712 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.712 band: 60
retest score: 0.717 0.717 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.717 band: 60
retest score: 0.676 0.676 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.676 band: 36
retest score: 0.668 0.668 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.668 band: 36
retest score: 0.706 0.706 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.706 band: 24
retest score: 0.751 0.751 features: ['Price_Var'] X_train.shape: (430, 22)
score: 0.751 band: 36

scores: 0.71 [0.626, 0.732, 0.712, 0.797, 0.712, 0.717, 0.676, 0.668, 0.706, 0.751]
retest score: 0.668 0.668 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.668 band: 48
retest score: 0.616 0.616 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.616 band: 24
retest score: 0.725 0.725 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.725 band: 60
retest score: 0.783 0.783 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.783 band: 60
retest score: 0.804 0.804 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.804 band: 60
retest score: 0.724 0.724 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.724 band: 36
retest score: 0.77 0.77 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.77 band: 60
retest score: 0.629 0.629 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.629 band: 24
retest score: 0.78 0.78 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.78 band: 24
retest score: 0.758 0.758 features: ['Buffet Indicator'] X_train.shape: (430, 22)
score: 0.758 band: 24

scores: 0.726 [0.668, 0.616, 0.725, 0.783, 0.804, 0.724, 0.77, 0.629, 0.78, 0.758]
retest score: 0.775 0.775 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.775 band: 12
retest score: 0.684 0.684 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.684 band: 48
retest score: 0.834 0.834 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.834 band: 60
retest score: 0.738 0.738 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.738 band: 60
retest score: 0.778 0.778 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.778 band: 60
retest score: 0.73 0.73 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.73 band: 60
retest score: 0.791 0.791 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.791 band: 60
retest score: 0.784 0.784 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.784 band: 24
retest score: 0.741 0.741 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.741 band: 60
retest score: 0.747 0.747 features: ['PER', 'Buffet Indicator'] X_train.shape: (430, 44)
score: 0.747 band: 60

scores: 0.76 [0.775, 0.684, 0.834, 0.738, 0.778, 0.73, 0.791, 0.784, 0.741, 0.747]
retest score: 0.722 0.722 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.722 band: 48
retest score: 0.779 0.779 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.779 band: 60
retest score: 0.787 0.787 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.787 band: 48
retest score: 0.797 0.797 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.797 band: 24
retest score: 0.816 0.816 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.816 band: 60
retest score: 0.778 0.778 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.778 band: 48
retest score: 0.739 0.739 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.739 band: 60
retest score: 0.765 0.765 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.765 band: 48
retest score: 0.768 0.768 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.768 band: 60
retest score: 0.821 0.821 features: ['PER', 'Price', 'Buffet Indicator'] X_train.shape: (430, 66)
score: 0.821 band: 24

scores: 0.777 [0.722, 0.779, 0.787, 0.797, 0.816, 0.778, 0.739, 0.765, 0.768, 0.821]

#

	Date	Value Value
0	Jan 13, 2023	29.19
1	Jan 1, 2023	27.96
2	Dec 1, 2022	28.65
3	Nov 1, 2022	28.74
4	Oct 1, 2022	27.35
...	...	...
1820	Jun 1, 1871	12.59
1821	May 1, 1871	12.59
1822	Apr 1, 1871	12.05
1823	Mar 1, 1871	11.19
1824	Feb 1, 1871	10.92

	Date	PER
0	Jan 13, 2023	29.19
1	Jan 1, 2023	27.96
2	Dec 1, 2022	28.65
3	Nov 1, 2022	28.74
4	Oct 1, 2022	27.35

	Date	Price Value
0	Jan 13, 2023	3991.94
1	Jan 1, 2023	3824.14
2	Dec 1, 2022	3912.38
3	Nov 1, 2022	3917.49
4	Oct 1, 2022	3726.05

	Date	Price
0	Jan 13, 2023	3991.94
1	Jan 1, 2023	3824.14
2	Dec 1, 2022	3912.38
3	Nov 1, 2022	3917.49
4	Oct 1, 2022	3726.05

	Date	PER	Price
0	Jan 13, 2023	29.19	3991.94
1	Jan 1, 2023	27.96	3824.14
2	Dec 1, 2022	28.65	3912.38
3	Nov 1, 2022	28.74	3917.49
4	Oct 1, 2022	27.35	3726.05
...	...	...	...
1820	Jun 1, 1871	12.59	4.82
1821	May 1, 1871	12.59	4.86
1822	Apr 1, 1871	12.05	4.74
1823	Mar 1, 1871	11.19	4.61
1824	Feb 1, 1871	10.92	4.50

	Date	PER	Price
0	2023-01-13	29.19	3991.94
1	2023-01-01	27.96	3824.14
2	2022-12-01	28.65	3912.38
3	2022-11-01	28.74	3917.49
4	2022-10-01	27.35	3726.05
...	...	...	...
1820	1871-06-01	12.59	4.82
1821	1871-05-01	12.59	4.86
1822	1871-04-01	12.05	4.74
1823	1871-03-01	11.19	4.61
1824	1871-02-01	10.92	4.50

	PER	Price
count	1825.000000	1825.000000
mean	17.005299	362.025249
std	7.065774	771.981468
min	4.780000	2.730000
25%	11.700000	7.930000
50%	15.910000	17.920000
75%	20.570000	179.400000
max	44.190000	4674.770000

	PER	Price	Date
Date
1871-11-01	11.60	4.64	1871-11-01
1871-10-01	11.47	4.59	1871-10-01
1871-09-01	12.31	4.84	1871-09-01
1871-08-01	12.55	4.79	1871-08-01
1871-07-01	12.27	4.73	1871-07-01
1871-06-01	12.59	4.82	1871-06-01
1871-05-01	12.59	4.86	1871-05-01
1871-04-01	12.05	4.74	1871-04-01
1871-03-01	11.19	4.61	1871-03-01
1871-02-01	10.92	4.50	1871-02-01

	PER	Price	Price0F	Price1F	Price2F	Price3F	Price4F	Price5F	Price6F	Price1P	Price_Var	PriceFmin	Price_Corr_6M
count	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000	1178.000000
mean	16.915085	314.127224	314.127224	310.867496	308.884711	308.165671	307.602453	307.759762	308.359593	313.395806	-0.000352	291.303175	0.073539
std	7.106650	715.704670	715.704670	708.638815	703.509252	701.083985	698.009447	696.476130	696.594704	713.260190	0.041136	660.757520	0.074120
min	4.780000	2.940000	2.940000	2.730000	2.730000	2.730000	2.730000	2.730000	2.730000	2.940000	-0.239709	2.730000	0.000244
25%	11.722500	7.135000	7.135000	7.042500	6.855000	6.812500	6.812500	6.820000	6.850000	7.435000	-0.020523	6.260000	0.020772
50%	15.895000	15.045000	15.045000	14.845000	14.755000	14.720000	14.690000	14.645000	14.355000	15.145000	0.001464	13.970000	0.050421
75%	20.312500	110.875000	110.875000	109.775000	109.700000	109.775000	109.775000	110.175000	109.775000	110.875000	0.023160	106.600000	0.101758
max	44.190000	4674.770000	4674.770000	4674.770000	4674.770000	4674.770000	4674.770000	4573.820000	4435.980000	4674.770000	0.502994	4435.980000	0.469682

	PER	Price	Date	Price0F	Price1F	Price2F	Price3F	Price4F	Price5F	Price6F	Price1P	Price_Var	PriceFmin	Price_Corr_6M
Date
2022-04-01	33.89	4391.30	2022-04-01	4391.30	4040.36	3898.95	3911.73	4158.56	3850.52	3726.05	4391.27	0.000007	3726.05	0.151493
2021-12-01	38.31	4674.77	2021-12-01	4674.77	4573.82	4435.98	4391.27	4391.30	4040.36	3898.95	4667.39	0.001581	3898.95	0.165959
2020-02-01	30.73	3277.31	2020-02-01	3277.31	2652.39	2761.98	2919.61	3104.66	3207.62	3391.71	3278.20	-0.000271	2652.39	0.190681
2020-01-01	30.99	3278.20	2020-01-01	3278.20	3277.31	2652.39	2761.98	2919.61	3104.66	3207.62	3176.75	0.031935	2652.39	0.190900
2019-12-01	30.33	3176.75	2019-12-01	3176.75	3278.20	3277.31	2652.39	2761.98	2919.61	3104.66	3104.90	0.023141	2652.39	0.165062
2008-10-01	16.39	968.80	2008-10-01	968.80	883.04	877.56	865.58	805.23	757.13	848.15	1216.95	-0.203911	757.13	0.218487
2008-09-01	20.36	1216.95	2008-09-01	1216.95	968.80	883.04	877.56	865.58	805.23	757.13	1281.47	-0.050348	757.13	0.377846
2008-08-01	21.40	1281.47	2008-08-01	1281.47	1216.95	968.80	883.04	877.56	865.58	805.23	1257.33	0.019199	805.23	0.371636
2008-07-01	20.91	1257.33	2008-07-01	1257.33	1281.47	1216.95	968.80	883.04	877.56	865.58	1341.25	-0.062568	865.58	0.311573
2008-06-01	22.42	1341.25	2008-06-01	1341.25	1257.33	1281.47	1216.95	968.80	883.04	877.56	1403.22	-0.044163	877.56	0.345715

	PER	Price	Date	Price0F	Price1F	Price2F	Price3F	Price4F	Price5F	Price6F	Price1P	Price_Var	PriceFmin	Price_Corr_6M
Date
2022-08-01	30.80	4158.56	2022-08-01	4158.56	3850.52	3726.05	3917.49	3912.38	3824.14	3991.94	3911.73	0.063100	3726.05	0.104005
2022-07-01	29.00	3911.73	2022-07-01	3911.73	4158.56	3850.52	3726.05	3917.49	3912.38	3824.14	3898.95	0.003278	3726.05	0.000000
2022-06-01	29.05	3898.95	2022-06-01	3898.95	3911.73	4158.56	3850.52	3726.05	3917.49	3912.38	4040.36	-0.034999	3726.05	0.000000
2022-05-01	30.67	4040.36	2022-05-01	4040.36	3898.95	3911.73	4158.56	3850.52	3726.05	3917.49	4391.30	-0.079917	3726.05	0.077793
2022-04-01	33.89	4391.30	2022-04-01	4391.30	4040.36	3898.95	3911.73	4158.56	3850.52	3726.05	4391.27	0.000007	3726.05	0.151493
2022-03-01	34.27	4391.27	2022-03-01	4391.27	4391.30	4040.36	3898.95	3911.73	4158.56	3850.52	4435.98	-0.010079	3850.52	0.123142
2022-02-01	35.29	4435.98	2022-02-01	4435.98	4391.27	4391.30	4040.36	3898.95	3911.73	4158.56	4573.82	-0.030137	3898.95	0.121062
2022-01-01	36.94	4573.82	2022-01-01	4573.82	4435.98	4391.27	4391.30	4040.36	3898.95	3911.73	4674.77	-0.021595	3898.95	0.147551
2021-12-01	38.31	4674.77	2021-12-01	4674.77	4573.82	4435.98	4391.27	4391.30	4040.36	3898.95	4667.39	0.001581	3898.95	0.165959
2021-11-01	38.58	4667.39	2021-11-01	4667.39	4674.77	4573.82	4435.98	4391.27	4391.30	4040.36	4460.71	0.046333	4040.36	0.134343

	Price_Corr_6M	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66
count	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000	872.000000
mean	0.030044	14.633773	23.853326	14.998240	16.275105	17.326078	18.053220	18.726086	19.427902	20.170131	20.982037	21.854472	22.844800	19.335511	2.522214	2.090115	-0.059436	-0.382652	19.521502	19.335511	2.522214	0.494389	0.418862	421.336869	788.154920	432.153034	472.318640	510.399461	539.300782	566.815765	595.098798	626.306628	660.584655	699.848689	744.879806	595.874069	101.839588	85.053755	0.041139	-0.612628	604.980657	595.874069	101.839588	15.636468	13.806622	-0.102297	0.084335	-0.068203	-0.028111	-0.014250	-0.004859	0.003221	0.011147	0.018187	0.025500	0.034142	0.045130	0.006764	0.033746	0.025385	-0.624053	2.223327	0.034596	0.026629	0.022181	0.032373	0.027091
std	0.059539	5.527819	9.166414	5.595779	6.001971	6.369740	6.582409	6.864662	7.245918	7.652873	8.173705	8.529445	8.868805	7.186275	1.562486	1.374145	0.670331	1.026186	7.284955	7.186275	1.562486	0.213507	0.192986	538.715148	999.346371	550.023969	595.525594	636.227301	669.185088	701.028948	734.373698	772.575259	819.129032	875.586215	939.275390	741.965112	132.766810	111.427794	0.547746	0.647745	753.065322	741.965112	132.766810	19.583186	18.884054	0.042212	0.021862	0.023436	0.011491	0.008541	0.006265	0.005850	0.005551	0.005342	0.004805	0.005287	0.007250	0.004995	0.006805	0.004902	0.730626	2.887010	0.006252	0.004443	0.004878	0.005799	0.005438
min	0.000000	6.640000	10.010000	6.649000	7.390800	7.839800	8.444000	8.757800	8.843600	8.979000	9.084800	9.260000	9.535600	8.579167	0.732325	0.594017	-2.482265	-1.599860	8.619747	8.579167	0.732325	0.221186	0.173401	13.930000	18.700000	13.945400	14.607200	14.780000	14.907600	15.153400	15.337600	15.765800	16.129200	16.842800	18.028400	15.689500	1.234354	0.991400	-1.408928	-1.727077	15.738145	15.689500	1.234354	0.455932	0.322354	-0.203911	0.046303	-0.113697	-0.058984	-0.037086	-0.021644	-0.009407	-0.003258	0.005858	0.015197	0.023998	0.029321	-0.005692	0.018549	0.014381	-3.194508	-0.619530	0.020565	0.015005	0.013153	0.018028	0.015464
25%	0.000000	9.070000	18.330000	9.624400	10.165600	11.004900	11.738900	12.230600	12.883100	13.852450	14.775300	16.073550	17.534800	13.298542	1.623506	1.311413	-0.500505	-1.049419	13.558322	13.298542	1.623506	0.359831	0.303020	66.720000	95.810000	68.357600	73.057000	78.535550	81.206300	84.657050	86.073000	87.569700	90.058400	91.889600	93.647600	84.523917	8.581077	6.933556	-0.394584	-1.094103	84.863550	84.523917	8.581077	1.787585	1.403782	-0.116844	0.068873	-0.085138	-0.034638	-0.020583	-0.008469	-0.001270	0.006866	0.015007	0.021247	0.030186	0.039719	0.003216	0.028625	0.021893	-1.045027	0.621206	0.030000	0.023858	0.018240	0.028510	0.023804
50%	0.000000	13.670000	22.280000	14.274800	16.076000	17.144000	17.733000	18.301800	19.069200	19.876500	20.421000	20.869500	21.458600	18.867500	2.117630	1.707564	-0.129921	-0.582408	18.992032	18.867500	2.117630	0.455678	0.373217	109.400000	238.250000	109.700000	117.380000	128.994000	141.478000	156.488000	163.930000	165.969000	168.506000	184.899000	203.898000	158.140000	32.034723	25.101167	0.017546	-0.696042	161.300028	158.140000	32.034723	4.207627	3.552464	-0.105555	0.073858	-0.058046	-0.026887	-0.013241	-0.004011	0.003474	0.011758	0.017818	0.026501	0.033724	0.044975	0.007346	0.032772	0.025075	-0.537754	1.151080	0.033605	0.026499	0.021147	0.031867	0.025974
75%	0.053825	19.677500	27.550000	19.867100	20.420800	21.258000	22.094000	23.362400	24.736000	25.607550	26.150000	26.523650	27.291200	23.745500	2.698728	2.308608	0.391757	-0.039917	23.946750	23.745500	2.698728	0.547161	0.451496	757.130000	1485.460000	812.955600	904.097200	992.479800	1073.387200	1121.644150	1175.589300	1250.155850	1323.213000	1381.794800	1447.572000	1156.179125	188.808509	153.310419	0.407805	-0.259992	1172.450701	1156.179125	188.808509	32.690000	24.725314	-0.067914	0.108439	-0.052437	-0.020673	-0.006782	-0.000723	0.007864	0.014234	0.022062	0.028942	0.038688	0.049935	0.009991	0.038565	0.028886	-0.209171	3.115929	0.039175	0.029778	0.025843	0.035903	0.030423
max	0.377846	27.670000	44.190000	28.657400	30.306800	32.317000	32.895200	34.569200	36.970400	38.803200	41.334400	42.320600	43.212800	36.634500	7.976569	7.214517	2.050546	7.139904	36.966912	36.634500	7.976569	1.190508	0.917348	2492.840000	4674.770000	2558.855800	2667.312000	2754.849800	2848.622400	2916.131800	3153.758000	3407.364200	3904.498800	4194.693200	4447.967600	3363.657833	706.784699	611.076472	1.612620	3.178645	3433.181349	3363.657833	706.784699	98.031186	96.553826	-0.035747	0.120217	-0.016349	-0.007115	-0.000274	0.011922	0.015437	0.024440	0.030495	0.036719	0.045736	0.062069	0.019740	0.050296	0.037867	1.828116	16.683022	0.049884	0.037836	0.033161	0.047505	0.039672

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66
Date
2023-01-13	24.82	38.58	26.182	28.4384	28.9968	29.5088	30.1846	30.9284	31.4830	33.1720	35.1722	37.3032	31.582333	3.372162	2.745544	0.562205	-0.548512	31.758869	31.582333	3.372162	0.937966	0.913907	2567.31	4674.77	2615.4900	2754.3908	2853.6954	2917.7128	3160.9430	3410.0632	3839.4404	3914.8328	4194.6932	4447.9676	3487.320667	660.814328	594.178378	0.257397	-1.381170	3548.352164	3487.320667	660.814328	106.524576	100.605073	-0.190681	0.063382	-0.078865	-0.032150	-0.016931	-0.000934	0.005774	0.015741	0.022035	0.030692	0.041262	0.056702	0.007410	0.041720	0.029198	-2.031210	7.610141	0.042030	0.030662	0.028988	0.039244	0.041012
2023-01-01	24.82	38.58	26.182	28.4384	28.9968	29.5752	30.3014	30.9836	31.7344	33.1720	35.1722	37.3032	31.629833	3.357935	2.722800	0.531863	-0.542356	31.804625	31.629833	3.357935	0.921017	0.917599	2567.31	4674.77	2615.4900	2725.7196	2803.7732	2903.5240	3104.8472	3383.3292	3811.3762	3912.0420	4194.6932	4447.9676	3465.874333	665.027041	596.659433	0.316468	-1.359323	3528.055391	3465.874333	665.027041	103.721017	101.161648	-0.190681	0.063382	-0.078865	-0.032167	-0.018250	-0.001328	0.003383	0.015507	0.020247	0.029374	0.039296	0.056702	0.006173	0.041721	0.029338	-1.963775	7.356023	0.041830	0.030437	0.028936	0.038618	0.040874
2022-12-01	24.82	38.58	26.182	28.6548	29.0490	29.5976	30.5952	31.0240	31.8564	33.5308	35.1722	37.3032	31.719000	3.329748	2.707467	0.488084	-0.539814	31.890397	31.719000	3.329748	0.930847	0.918179	2567.31	4674.77	2615.4900	2725.7196	2793.5632	2901.0200	3080.7790	3337.5776	3765.3160	3912.0420	4194.6932	4447.9676	3448.635333	668.977061	599.657711	0.370096	-1.347005	3511.859498	3448.635333	668.977061	103.660000	101.172052	-0.190681	0.063382	-0.078865	-0.032167	-0.016931	-0.000934	0.005774	0.015741	0.022035	0.030692	0.041262	0.056702	0.007334	0.041877	0.029389	-2.005977	7.446348	0.042169	0.030846	0.028996	0.039570	0.041112
2022-11-01	24.82	38.58	26.182	28.7124	29.2264	29.8112	30.7168	31.1216	31.9770	33.5308	35.1722	37.3032	31.776333	3.305537	2.672056	0.459111	-0.512261	31.944950	31.776333	3.305537	0.950000	0.912205	2567.31	4674.77	2615.4900	2706.6056	2789.7132	2896.6204	2993.0410	3277.9152	3713.1392	3911.0956	4194.6932	4447.9676	3427.834667	673.699477	601.533444	0.424836	-1.317205	3492.328302	3427.834667	673.699477	105.699831	100.374629	-0.190681	0.063382	-0.078865	-0.032167	-0.016931	-0.000345	0.006856	0.016582	0.023071	0.030692	0.041262	0.056702	0.007810	0.041939	0.029514	-2.030223	7.481989	0.042315	0.031278	0.028740	0.039013	0.041153
2022-10-01	24.82	38.58	26.182	28.7204	29.2398	29.9720	30.7846	31.2144	31.9770	33.5308	35.1722	37.3032	31.819000	3.282119	2.636800	0.442491	-0.475400	31.985020	31.819000	3.282119	0.939831	0.910586	2567.31	4674.77	2596.0904	2702.9612	2784.9904	2886.2880	2981.1744	3255.0092	3633.8556	3904.4988	4194.6932	4447.9676	3405.770000	679.049006	603.294333	0.476668	-1.277599	3471.698679	3405.770000	679.049006	103.653898	99.834902	-0.190681	0.063382	-0.078865	-0.032167	-0.016931	-0.000345	0.006856	0.015741	0.022035	0.029374	0.039296	0.056702	0.007192	0.041558	0.028979	-2.060638	7.722770	0.041833	0.030661	0.028699	0.037814	0.040863

	GDP	Wilshire 5000
1946-01-01	NaN	NaN
1946-01-02	NaN	NaN
1946-01-03	NaN	NaN
1946-01-04	NaN	NaN
1946-01-05	NaN	NaN
...	...	...
2023-01-08	NaN	38642.433333
2023-01-09	NaN	38648.300000
2023-01-10	NaN	38950.460000
2023-01-11	NaN	39470.430000
2023-01-12	NaN	39663.090000

	GDP	Wilshire 5000
1970-12-31	1134.650043	830.270000
1971-01-01	1135.156000	831.754138
1971-01-02	1135.390611	833.238276
1971-01-03	1135.625222	834.722414
1971-01-04	1135.859833	836.206552
...	...	...
2023-01-08	NaN	38642.433333
2023-01-09	NaN	38648.300000
2023-01-10	NaN	38950.460000
2023-01-11	NaN	39470.430000
2023-01-12	NaN	39663.090000

	PER	Price	Price_Var	horizon	PC6M1	PC6M2	PC6M3	PC6M4	PC6M5	PC6M6	PC6M7	PC6M8	PC6M9	PC6M10	PC6M_AVG
Date
2023-01-13	29.19	3991.94	0.043879	future	0.10	0.11	0.08	0.1	0.09	0.11	0.09	0.01	0.18	0.06	0.093
2023-01-01	27.96	3824.14	-0.022554	future	0.10	0.11	0.07	0.1	0.08	0.10	0.08	-0.00	0.19	0.06	0.089
2022-12-01	28.65	3912.38	-0.001304	future	0.10	0.11	0.08	0.1	0.08	0.10	0.09	0.01	0.11	0.06	0.084
2022-11-01	28.74	3917.49	0.051379	future	0.13	0.12	0.11	0.1	0.10	0.10	0.11	0.02	0.11	0.09	0.099
2022-10-01	27.35	3726.05	-0.032326	future	0.13	0.12	0.11	0.1	0.10	0.07	0.10	0.01	0.11	0.09	0.094
2022-09-01	28.42	3850.52	-0.074074	future	0.13	0.11	0.10	0.1	0.08	0.07	0.09	0.02	0.10	0.09	0.089

	Date	PER	Price	Price_Var	horizon	Buffet Indicator	PC6M1	PC6M2	PC6M3	PC6M4	PC6M5	PC6M6	PC6M7	PC6M8	PC6M9	PC6M10	PC6M_AVG
2023-01-13	2023-01-13	29.19	3991.94	0.043879	future	1.485784	0.03	0.1	0.00	0.10	0.09	0.02	0.02	0.11	0.03	0.11	0.061
2023-01-01	2023-01-01	27.96	3824.14	-0.022554	future	1.425834	0.07	0.1	0.00	0.12	0.13	0.02	0.02	0.12	0.03	0.12	0.073
2022-12-01	2022-12-01	28.65	3912.38	-0.001304	future	1.530109	0.05	0.1	0.00	0.11	0.11	0.02	0.03	0.13	0.03	0.09	0.067
2022-11-01	2022-11-01	28.74	3917.49	0.051379	future	1.464387	0.06	0.1	0.03	0.10	0.11	0.03	0.04	0.15	0.06	0.03	0.071
2022-10-01	2022-10-01	27.35	3726.05	-0.032326	future	1.379913	0.06	0.1	0.03	0.09	0.12	0.02	0.04	0.19	0.05	0.12	0.082
2022-09-01	2022-09-01	28.42	3850.52	-0.074074	future	1.519690	0.10	0.1	0.04	0.10	0.09	0.04	0.04	0.13	0.05	0.03	0.072

	Date	PER	Price	Price_Var	horizon	Buffet Indicator	PC6M1	PC6M2	PC6M3	PC6M4	PC6M5	PC6M6	PC6M7	PC6M8	PC6M9	PC6M10	PC6M_AVG
2023-01-13	2023-01-13	29.19	3991.94	0.043879	future	1.485784	0.09	0.05	0.07	0.1	0.08	0.10	0.13	0.11	0.12	0.10	0.095
2023-01-01	2023-01-01	27.96	3824.14	-0.022554	future	1.425834	0.09	0.05	0.07	0.1	0.07	0.10	0.13	0.10	0.12	0.09	0.092
2022-12-01	2022-12-01	28.65	3912.38	-0.001304	future	1.530109	0.08	0.05	0.06	0.1	0.07	0.10	0.13	0.10	0.12	0.09	0.090
2022-11-01	2022-11-01	28.74	3917.49	0.051379	future	1.464387	0.09	0.05	0.06	0.1	0.10	0.10	0.10	0.10	0.12	0.10	0.092
2022-10-01	2022-10-01	27.35	3726.05	-0.032326	future	1.379913	0.09	0.05	0.06	0.1	0.11	0.10	0.10	0.10	0.11	0.10	0.092
2022-09-01	2022-09-01	28.42	3850.52	-0.074074	future	1.519690	0.08	0.04	0.06	0.1	0.10	0.11	0.10	0.10	0.10	0.10	0.089

	Date	PER	Price	Price_Var	horizon	Buffet Indicator	PC6M1	PC6M2	PC6M3	PC6M4	PC6M5	PC6M6	PC6M7	PC6M8	PC6M9	PC6M10	PC6M_AVG
2023-01-13	2023-01-13	29.19	3991.94	0.043879	future	1.485784	0.06	0.05	0.09	0.08	0.07	0.09	0.07	0.09	0.03	0.04	0.067
2023-01-01	2023-01-01	27.96	3824.14	-0.022554	future	1.425834	0.07	0.05	0.11	0.12	0.07	0.06	0.04	0.06	0.04	0.08	0.070
2022-12-01	2022-12-01	28.65	3912.38	-0.001304	future	1.530109	0.08	0.05	0.05	0.07	0.07	0.08	0.07	0.08	0.04	0.08	0.067
2022-11-01	2022-11-01	28.74	3917.49	0.051379	future	1.464387	0.08	0.05	0.04	0.07	0.07	0.09	0.07	0.07	0.04	0.08	0.066
2022-10-01	2022-10-01	27.35	3726.05	-0.032326	future	1.379913	0.08	0.05	0.04	0.07	0.07	0.07	0.07	0.07	0.05	0.07	0.064
2022-09-01	2022-09-01	28.42	3850.52	-0.074074	future	1.519690	0.08	0.05	0.04	0.07	0.07	0.09	0.07	0.08	0.04	0.07	0.066